Datathon NSI Mentors’ Guidelines – Economic Time Series Prediction
In this article the mentors give some preliminary guidelines, advice and suggestions to the participants for the case. Every mentor should write […]
Author: sist
Datathon Telenor Mentors’ Guidelines – On TelCo predictions
In this article the mentors give some preliminary guidelines, advice and suggestions to the participants for the case. Every mentor should write […]
Datathon Sofia Air Mentors’ Guidelines – On IOT Prediction
In this article the mentors give some preliminary guidelines, advice and suggestions to the participants for the case. Every mentor should write […]
Datathon Kaufland Mentors’ Guidelines – On Predictive Maintenance
In this article, the mentors give some preliminary guidelines, advice, and suggestions to the participants for the case. Every mentor should write […]
Datathon Kaufland Solution – Kaufland case – Team3
Kaufland Case – Team3 No content
The Kaufland Case [Global Datathon 2018] – Guidelines by Simon Stiebellehner
The Kaufland Case poses an interesting Predictive Maintenance challenge. First, make sure that you understand what the goals and deliverables are. This […]
Datathon Kaufland Solution – Team Total Kaputt! – Why da faQ the machine broke down?
What we tried to do to solve the Kaufland case for the Global Datathon 2018. This article just contains our exploratory data analysis in the form of many plots and some explanations. There isn’t any modeling stage described here.
Datathon Ontotext Mentors’ Guidelines – Text Mining Classification
In this article the mentors give some preliminary guidelines, advice and suggestions to the participants for the case. Every mentor should write […]
Datathon Kaufland Solution – Predictive Maintenance Based on Sensor Data for Forklifts
Kaufland-Case 1. Business Understanding Industrial vibration analysis is a measurement tool used to identify, predict, and prevent failures. Implementing vibration analysis on […]
Datathon Kaufland Solution – LSTM and EDM Models for Predictive Maintenance
In this paper we propose the use of a combination of LSTM and EDM models to address the issue of anomaly classification and prediction in time series data. Working with sensor data for automated storage and retrieval systems for a German hypermarket chain, we show that predictors based on variance and median methods show sufficient promise in the handling of anomalies.